Results 161 to 170 of about 15,812 (298)
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li +4 more
wiley +1 more source
Optimized shaped pulses for a 2D single-frequency technique for refocusing (SIFTER). [PDF]
Trenkler PAS +3 more
europepmc +1 more source
Abstract This work experimentally validates the RESPONSE (Resilient Process cONtrol SystEm) framework as a solution for maintaining safe, continuous operation of cyber‐physical process systems under cyberattacks. RESPONSE implements a dual‐loop architecture that runs a networked online controller in parallel with a hard‐isolated offline controller ...
Luyang Liu +5 more
wiley +1 more source
Ultrabroadband Spacetime Nanoscopy of Terahertz Polaritons in a van der Waals Cavity. [PDF]
Wehmeier L +17 more
europepmc +1 more source
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj +2 more
wiley +1 more source
Solid-State Vortex Laser at Eye-Safe Band: A Perspective. [PDF]
Zhang H, Zhang Z, Hu L, Gao C, Fu S.
europepmc +1 more source
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
Semi-Supervised Radar Work Mode Recognition Based on Contrastive Learning. [PDF]
Sun P, Du M, Li Z, Chen X, Shi J.
europepmc +1 more source
This work shows resonant tunneling diode‐based opto‐electronic spiking neurons enabling fast edge detection in time series, a two‐layer photonic spiking neural network for complex classification, and a depth‐tunable photonic spiking memory system. Neuromorphic computing—modeled after the functionality and efficiency of biological neural systems—offers ...
Dafydd Owen‐Newns +8 more
wiley +1 more source

